Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor Principal Component Analysis
This addresses feature extraction challenges in hyperspectral image analysis, offering improved classification performance for remote sensing applications, though it appears incremental as an extension of PCA.
The paper tackled spectral-spatial feature extraction for hyperspectral image classification by introducing Tensor Principal Component Analysis (TPCA), which extended PCA to a tensor-based framework with circular convolution. Experimental results showed that classification models using TPCA features consistently outperformed traditional PCA and other state-of-the-art techniques on benchmark datasets.
This paper addresses the challenge of spectral-spatial feature extraction for hyperspectral image classification by introducing a novel tensor-based framework. The proposed approach incorporates circular convolution into a tensor structure to effectively capture and integrate both spectral and spatial information. Building upon this framework, the traditional Principal Component Analysis (PCA) technique is extended to its tensor-based counterpart, referred to as Tensor Principal Component Analysis (TPCA). The proposed TPCA method leverages the inherent multi-dimensional structure of hyperspectral data, thereby enabling more effective feature representation. Experimental results on benchmark hyperspectral datasets demonstrate that classification models using TPCA features consistently outperform those using traditional PCA and other state-of-the-art techniques. These findings highlight the potential of the tensor-based framework in advancing hyperspectral image analysis.